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A new method for reducing local heat transfer data in multimicrochannel evaporators
Measuring the strong local variation in heat transfer coefficients in multi-microchannel evaporators is related to the inverse heat conduction problem (IHCP). As the local flow heat transfer coefficients change greatly in magnitude from single-phase liquid at the entrance to a peak in slug flow and then to a minimum at the transition to the onset of annular flow and finally a new substantial rise up to the outlet, a significant heat spreading occurs due to the heat transfer process itself, and this has to be accounted for when processing the data. Until now, IHCP has not been introduced in the experimental study of heat transfer in such evaporators when reducing local experimental data. In this paper, a new method for processing experimental local heat transfer data by solving the 3D IHCP is proposed. This method is then applied and validated Using two sets of single-and two-phase flow experimental data obtained with infrared (IR) camera temperature measurements. The 14 400 raw pixel temperatures per image from the IR camera are first pre-processed by a filtering technique to remove the noise and then to smooth the data, where the IR camera has undergone a prior inhouse pixel by pixel insitu temperature calibration. Three filtering techniques (Wiener filter, spline smooth, and polynomial surface fitting) are compared. The polynomial surface fitting technique was shown to be more suitable for the current type of data set. Then the 3D IHCP is solved based on a finite volume method using the TDMA (Tridiagonal Matrix Algorithm) solver with a combination of Newton-Raphson iteration and a local energy balance method. Furthermore, the present 3D TDMA method (named as 3D TDMA) is compared with three other post processing methods currently used in the literature, among which the present one is found to be more accurate for reducing the local heat transfer data in multi-microchannel evaporators. (C) 2017 Elsevier Masson SAS. All rights reserved.